import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class  A_			(							snake_case_			):
  _SCREAMING_SNAKE_CASE     					=      (DEISMultistepScheduler,)
  _SCREAMING_SNAKE_CASE     					=      (('num_inference_steps', 25),)

  def 		_UpperCAmelCase (		self			:   Dict  ,				**__SCREAMING_SNAKE_CASE			:   List[str]							):
   __a							=	{
       '''num_train_timesteps''': 10_00,
       '''beta_start''': 0.00_01,
       '''beta_end''': 0.02,
       '''beta_schedule''': '''linear''',
       '''solver_order''': 2,
   }

   config.update(**lowerCAmelCase__							)
   return config

  def 		_UpperCAmelCase (		self			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   Dict=0  ,				**__SCREAMING_SNAKE_CASE			:   Tuple							):
   __a							=	dict(self.forward_default_kwargs							)
   __a							=	kwargs.pop("num_inference_steps"  ,				lowerCAmelCase__							)
   __a							=	self.dummy_sample
   __a							=	0.1 * sample
   __a							=	[residual + 0.2, residual + 0.15, residual + 0.10]

   for scheduler_class in self.scheduler_classes:
    __a							=	self.get_scheduler_config(**lowerCAmelCase__							)
    __a							=	scheduler_class(**lowerCAmelCase__							)
    scheduler.set_timesteps(lowerCAmelCase__							)
    # copy over dummy past residuals
    __a							=	dummy_past_residuals[: scheduler.config.solver_order]

    with tempfile.TemporaryDirectory() as tmpdirname:
     scheduler.save_config(lowerCAmelCase__							)
     __a							=	scheduler_class.from_pretrained(lowerCAmelCase__							)
     new_scheduler.set_timesteps(lowerCAmelCase__							)
     # copy over dummy past residuals
     __a							=	dummy_past_residuals[: new_scheduler.config.solver_order]

    __a							=	sample, sample
    for t in range(lowerCAmelCase__  ,				time_step + scheduler.config.solver_order + 1							):
     __a							=	scheduler.step(lowerCAmelCase__  ,				lowerCAmelCase__  ,				lowerCAmelCase__  ,				**lowerCAmelCase__							).prev_sample
     __a							=	new_scheduler.step(lowerCAmelCase__  ,				lowerCAmelCase__  ,				lowerCAmelCase__  ,				**lowerCAmelCase__							).prev_sample

     assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

  def 		_UpperCAmelCase (		self			:   Optional[Any]							):
   pass

  def 		_UpperCAmelCase (		self			:   Any  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0  ,				**__SCREAMING_SNAKE_CASE			:   str							):
   __a							=	dict(self.forward_default_kwargs							)
   __a							=	kwargs.pop("num_inference_steps"  ,				lowerCAmelCase__							)
   __a							=	self.dummy_sample
   __a							=	0.1 * sample
   __a							=	[residual + 0.2, residual + 0.15, residual + 0.10]

   for scheduler_class in self.scheduler_classes:
    __a							=	self.get_scheduler_config()
    __a							=	scheduler_class(**lowerCAmelCase__							)
    scheduler.set_timesteps(lowerCAmelCase__							)

    # copy over dummy past residuals (must be after setting timesteps)
    __a							=	dummy_past_residuals[: scheduler.config.solver_order]

    with tempfile.TemporaryDirectory() as tmpdirname:
     scheduler.save_config(lowerCAmelCase__							)
     __a							=	scheduler_class.from_pretrained(lowerCAmelCase__							)
     # copy over dummy past residuals
     new_scheduler.set_timesteps(lowerCAmelCase__							)

     # copy over dummy past residual (must be after setting timesteps)
     __a							=	dummy_past_residuals[: new_scheduler.config.solver_order]

    __a							=	scheduler.step(lowerCAmelCase__  ,				lowerCAmelCase__  ,				lowerCAmelCase__  ,				**lowerCAmelCase__							).prev_sample
    __a							=	new_scheduler.step(lowerCAmelCase__  ,				lowerCAmelCase__  ,				lowerCAmelCase__  ,				**lowerCAmelCase__							).prev_sample

    assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

  def 		_UpperCAmelCase (		self			:   Any  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=None  ,				**__SCREAMING_SNAKE_CASE			:   List[str]							):
   if scheduler is None:
    __a							=	self.scheduler_classes[0]
    __a							=	self.get_scheduler_config(**lowerCAmelCase__							)
    __a							=	scheduler_class(**lowerCAmelCase__							)

   __a							=	self.scheduler_classes[0]
   __a							=	self.get_scheduler_config(**lowerCAmelCase__							)
   __a							=	scheduler_class(**lowerCAmelCase__							)

   __a							=	10
   __a							=	self.dummy_model()
   __a							=	self.dummy_sample_deter
   scheduler.set_timesteps(lowerCAmelCase__							)

   for i, t in enumerate(scheduler.timesteps							):
    __a							=	model(lowerCAmelCase__  ,				lowerCAmelCase__							)
    __a							=	scheduler.step(lowerCAmelCase__  ,				lowerCAmelCase__  ,				lowerCAmelCase__							).prev_sample

   return sample

  def 		_UpperCAmelCase (		self			:   Dict							):
   __a							=	dict(self.forward_default_kwargs							)

   __a							=	kwargs.pop("num_inference_steps"  ,				lowerCAmelCase__							)

   for scheduler_class in self.scheduler_classes:
    __a							=	self.get_scheduler_config()
    __a							=	scheduler_class(**lowerCAmelCase__							)

    __a							=	self.dummy_sample
    __a							=	0.1 * sample

    if num_inference_steps is not None and hasattr(lowerCAmelCase__  ,				"set_timesteps"							):
     scheduler.set_timesteps(lowerCAmelCase__							)
    elif num_inference_steps is not None and not hasattr(lowerCAmelCase__  ,				"set_timesteps"							):
     __a							=	num_inference_steps

    # copy over dummy past residuals (must be done after set_timesteps)
    __a							=	[residual + 0.2, residual + 0.15, residual + 0.10]
    __a							=	dummy_past_residuals[: scheduler.config.solver_order]

    __a							=	scheduler.timesteps[5]
    __a							=	scheduler.timesteps[6]

    __a							=	scheduler.step(lowerCAmelCase__  ,				lowerCAmelCase__  ,				lowerCAmelCase__  ,				**lowerCAmelCase__							).prev_sample
    __a							=	scheduler.step(lowerCAmelCase__  ,				lowerCAmelCase__  ,				lowerCAmelCase__  ,				**lowerCAmelCase__							).prev_sample

    self.assertEqual(output_a.shape  ,				sample.shape							)
    self.assertEqual(output_a.shape  ,				output_a.shape							)

  def 		_UpperCAmelCase (		self			:   List[str]							):
   __a							=	DEISMultistepScheduler(**self.get_scheduler_config()							)
   __a							=	self.full_loop(scheduler=lowerCAmelCase__							)
   __a							=	torch.mean(torch.abs(lowerCAmelCase__							)							)

   assert abs(result_mean.item() - 0.2_39_16							) < 1E-3

   __a							=	DPMSolverSinglestepScheduler.from_config(scheduler.config							)
   __a							=	DPMSolverMultistepScheduler.from_config(scheduler.config							)
   __a							=	UniPCMultistepScheduler.from_config(scheduler.config							)
   __a							=	DEISMultistepScheduler.from_config(scheduler.config							)

   __a							=	self.full_loop(scheduler=lowerCAmelCase__							)
   __a							=	torch.mean(torch.abs(lowerCAmelCase__							)							)

   assert abs(result_mean.item() - 0.2_39_16							) < 1E-3

  def 		_UpperCAmelCase (		self			:   Dict							):
   for timesteps in [25, 50, 1_00, 9_99, 10_00]:
    self.check_over_configs(num_train_timesteps=lowerCAmelCase__							)

  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   self.check_over_configs(thresholding=lowerCAmelCase__							)
   for order in [1, 2, 3]:
    for solver_type in ["logrho"]:
     for threshold in [0.5, 1.0, 2.0]:
      for prediction_type in ["epsilon", "sample"]:
       self.check_over_configs(
           thresholding=lowerCAmelCase__  ,				prediction_type=lowerCAmelCase__  ,				sample_max_value=lowerCAmelCase__  ,				algorithm_type="deis"  ,				solver_order=lowerCAmelCase__  ,				solver_type=lowerCAmelCase__  ,				)

  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   for prediction_type in ["epsilon", "v_prediction"]:
    self.check_over_configs(prediction_type=lowerCAmelCase__							)

  def 		_UpperCAmelCase (		self			:   List[Any]							):
   for algorithm_type in ["deis"]:
    for solver_type in ["logrho"]:
     for order in [1, 2, 3]:
      for prediction_type in ["epsilon", "sample"]:
       self.check_over_configs(
           solver_order=lowerCAmelCase__  ,				solver_type=lowerCAmelCase__  ,				prediction_type=lowerCAmelCase__  ,				algorithm_type=lowerCAmelCase__  ,				)
       __a							=	self.full_loop(
           solver_order=lowerCAmelCase__  ,				solver_type=lowerCAmelCase__  ,				prediction_type=lowerCAmelCase__  ,				algorithm_type=lowerCAmelCase__  ,				)
       assert not torch.isnan(lowerCAmelCase__							).any(), "Samples have nan numbers"

  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   self.check_over_configs(lower_order_final=lowerCAmelCase__							)
   self.check_over_configs(lower_order_final=lowerCAmelCase__							)

  def 		_UpperCAmelCase (		self			:   List[str]							):
   for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
    self.check_over_forward(num_inference_steps=lowerCAmelCase__  ,				time_step=0							)

  def 		_UpperCAmelCase (		self			:   Any							):
   __a							=	self.full_loop()
   __a							=	torch.mean(torch.abs(lowerCAmelCase__							)							)

   assert abs(result_mean.item() - 0.2_39_16							) < 1E-3

  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   __a							=	self.full_loop(prediction_type="v_prediction"							)
   __a							=	torch.mean(torch.abs(lowerCAmelCase__							)							)

   assert abs(result_mean.item() - 0.0_91							) < 1E-3

  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   __a							=	self.scheduler_classes[0]
   __a							=	self.get_scheduler_config(thresholding=lowerCAmelCase__  ,				dynamic_thresholding_ratio=0							)
   __a							=	scheduler_class(**lowerCAmelCase__							)

   __a							=	10
   __a							=	self.dummy_model()
   __a							=	self.dummy_sample_deter.half()
   scheduler.set_timesteps(lowerCAmelCase__							)

   for i, t in enumerate(scheduler.timesteps							):
    __a							=	model(lowerCAmelCase__  ,				lowerCAmelCase__							)
    __a							=	scheduler.step(lowerCAmelCase__  ,				lowerCAmelCase__  ,				lowerCAmelCase__							).prev_sample

   assert sample.dtype == torch.floataa







'''simple docstring'''



import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
							import tensorflow as tf

if is_tensorflow_text_available():
							from transformers.models.bert import TFBertTokenizer


lowerCAmelCase_  :							List[str]             =    ['bert-base-uncased', 'bert-base-cased']
lowerCAmelCase_  :							int             =    'hf-internal-testing/tiny-bert-tf-only'

if is_tf_available():
							class 						lowerCamelCase_				(       tf.keras.Model      ):







													def __init__(	self :   Optional[int]					,     lowerCAmelCase__ :   Optional[Any]					):

																				"""simple docstring"""
																				super().__init__()
																				SCREAMING_SNAKE_CASE       :   List[str]         =      tokenizer
																				SCREAMING_SNAKE_CASE       :   str         =      AutoConfig.from_pretrained(lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      TFAutoModel.from_config(lowerCAmelCase__					)







													def        __lowercase     (	self :   Tuple					,     lowerCAmelCase__ :   Tuple					):

																				"""simple docstring"""
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.tokenizer(lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   str         =      self.bert(**lowerCAmelCase__					)
																				return out["pooler_output"]
@require_tf
@require_tensorflow_text
class 						lowerCamelCase_				(       unittest.TestCase      ):







						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													super().setUp()

													SCREAMING_SNAKE_CASE       :   int         =      [
													    BertTokenizer.from_pretrained(lowerCAmelCase__					) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
													]  # repeat for when fast_bert_tokenizer=false
													SCREAMING_SNAKE_CASE       :   Dict         =      [TFBertTokenizer.from_pretrained(lowerCAmelCase__					) for checkpoint in TOKENIZER_CHECKPOINTS] + [
													    TFBertTokenizer.from_pretrained(lowerCAmelCase__					,     use_fast_bert_tokenizer=lowerCAmelCase__					)
													    for checkpoint in TOKENIZER_CHECKPOINTS
													]
													assert len(self.tokenizers					) == len(self.tf_tokenizers					)

													SCREAMING_SNAKE_CASE       :   Any         =      [
													    '''This is a straightforward English test sentence.''',
													    '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
													    '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
													    '''And some much more rare Chinese: 齉 堃 齉堃''',
													    '''Je vais aussi écrire en français pour tester les accents''',
													    '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
													]
													SCREAMING_SNAKE_CASE       :   int         =      list(zip(self.test_sentences					,     self.test_sentences[::-1]					)					)







						def        __lowercase     (	self :   Any					):

													"""simple docstring"""
													for tokenizer, tf_tokenizer in zip(self.tokenizers					,     self.tf_tokenizers					):
																				for test_inputs in (self.test_sentences, self.paired_sentences):
																											SCREAMING_SNAKE_CASE       :   Tuple         =      tokenizer(lowerCAmelCase__					,     return_tensors='''tf'''					,     padding='''longest'''					)
																											SCREAMING_SNAKE_CASE       :   List[Any]         =      tf_tokenizer(lowerCAmelCase__					)

																											for key in python_outputs.keys():
																																		self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape					)					)
																																		self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]					,     tf.intaa					) == tf_outputs[key]					)					)







						@slow
						def        __lowercase     (	self :   Dict					):

													"""simple docstring"""
													for tf_tokenizer in self.tf_tokenizers:
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      tf_tokenizer(self.paired_sentences					)
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      tf_tokenizer(
																				    text=[sentence[0] for sentence in self.paired_sentences]					,     text_pair=[sentence[1] for sentence in self.paired_sentences]					,     )
																				for key in merged_outputs.keys():
																											self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]					,     tf.intaa					) == separated_outputs[key]					)					)







						@slow
						def        __lowercase     (	self :   Union[str, Any]					):

													"""simple docstring"""
													for tf_tokenizer in self.tf_tokenizers:
																				SCREAMING_SNAKE_CASE       :   Any         =      tf.function(lowerCAmelCase__					)
																				for test_inputs in (self.test_sentences, self.paired_sentences):
																											SCREAMING_SNAKE_CASE       :   int         =      tf.constant(lowerCAmelCase__					)
																											SCREAMING_SNAKE_CASE       :   Any         =      compiled_tokenizer(lowerCAmelCase__					)
																											SCREAMING_SNAKE_CASE       :   List[str]         =      tf_tokenizer(lowerCAmelCase__					)

																											for key in eager_outputs.keys():
																																		self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]					)					)







						@slow
						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													for tf_tokenizer in self.tf_tokenizers:
																				SCREAMING_SNAKE_CASE       :   int         =      ModelToSave(tokenizer=lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   Tuple         =      tf.convert_to_tensor(self.test_sentences					)
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      model(lowerCAmelCase__					)  # Build model with some sample inputs
																				with TemporaryDirectory() as tempdir:
																											SCREAMING_SNAKE_CASE       :   Optional[int]         =      Path(lowerCAmelCase__					) / '''saved.model'''
																											model.save(lowerCAmelCase__					)
																											SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      tf.keras.models.load_model(lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   Tuple         =      loaded_model(lowerCAmelCase__					)
																				# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
																				self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output					)					)					,     1e-5					)




